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A Smart-Distributed Pareto Front Using ev-MOGA Evolutionary Algorithm

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A Smart-Distributed Pareto Front Using ev-MOGA Evolutionary Algorithm

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dc.contributor.author Herrero Durá, Juan Manuel es_ES
dc.contributor.author Reynoso Meza, Gilberto es_ES
dc.contributor.author Martínez Iranzo, Miguel Andrés es_ES
dc.contributor.author Blasco Ferragud, Francesc Xavier es_ES
dc.contributor.author Sanchís Saez, Javier es_ES
dc.date.accessioned 2020-05-29T03:32:28Z
dc.date.available 2020-05-29T03:32:28Z
dc.date.issued 2014-04 es_ES
dc.identifier.issn 0218-2130 es_ES
dc.identifier.uri http://hdl.handle.net/10251/144562
dc.description.abstract [EN] Obtaining multi-objective optimization solutions with a small number of points smartly distributed along the Pareto front is a challenge. Optimization methods, such as the nor- malized normal constraint (NNC), propose the use of a filter to achieve a smart Pareto front distribution. The NCC optimization method presents several disadvantages related with the procedure itself, initial condition dependency, and computational burden. In this article, the epsilon-variable multi-objective genetic algorithm (ev-MOGA) is pre- sented. This algorithm characterizes the Pareto front in a smart way and removes the disadvantages of the NNC method. Finally, examples of a three-bar truss design and controller tuning optimizations are presented for comparison purposes. es_ES
dc.description.sponsorship This work was partially supported by the FPI-2010/19 grant and the PAID-06-11 project from the Universitat Politècnica de València, projects TIN2011-28082 and ENE2011-25900 (Spanish Ministry of Economy and Competitiveness) and the GV/2012/073 (Generalitat Valenciana). es_ES
dc.language Inglés es_ES
dc.publisher World Scientific es_ES
dc.relation.ispartof International Journal of Artificial Intelligence Tools es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Multi-objective optimization es_ES
dc.subject Pareto front es_ES
dc.subject Engineering design es_ES
dc.subject Evolutionary algorithms es_ES
dc.subject Multi-objective evolutionary algorithms es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title A Smart-Distributed Pareto Front Using ev-MOGA Evolutionary Algorithm es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1142/S021821301450002X es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//FPI%2F2010%2F19/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-06-11/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//ENE2011-25900/ES/GESTION OPTIMA MEDIANTE CONTROLADORES AVANZADOS DE PILAS DE COMBUSTIBLE TIPO PEM PARA APLICACIONES MOVILES Y ESTATICAS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TIN2011-28082/ES/DISEÑO E IMPLEMENTACION DE PILOTOS AUTOMATICOS PARA VEHICULOS AEREOS NO TRIPULADOS (UAVS) MEDIANTE TECNICAS DE OPTIMIZACION Y CONTROL AVANZADO/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//GV%2F2012%2F073/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica es_ES
dc.description.bibliographicCitation Herrero Durá, JM.; Reynoso Meza, G.; Martínez Iranzo, MA.; Blasco Ferragud, FX.; Sanchís Saez, J. (2014). A Smart-Distributed Pareto Front Using ev-MOGA Evolutionary Algorithm. International Journal of Artificial Intelligence Tools. 23(2):1-22. https://doi.org/10.1142/S021821301450002X es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1142/S021821301450002X es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 22 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 23 es_ES
dc.description.issue 2 es_ES
dc.relation.pasarela S\267291 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.description.references Marler, R. T., & Arora, J. S. (2009). The weighted sum method for multi-objective optimization: new insights. Structural and Multidisciplinary Optimization, 41(6), 853-862. doi:10.1007/s00158-009-0460-7 es_ES
dc.description.references Messac, A., & Mattson, C. A. (2002). Optimization and Engineering, 3(4), 431-450. doi:10.1023/a:1021179727569 es_ES
dc.description.references Messac, A., Ismail-Yahaya, A., & Mattson, C. A. (2003). The normalized normal constraint method for generating the Pareto frontier. Structural and Multidisciplinary Optimization, 25(2), 86-98. doi:10.1007/s00158-002-0276-1 es_ES
dc.description.references Martínez, M., Sanchis, J., & Blasco, X. (2006). Global and well-distributed Pareto frontier by modified normalized normal constraint methods for bicriterion problems. Structural and Multidisciplinary Optimization, 34(3), 197-209. doi:10.1007/s00158-006-0071-5 es_ES
dc.description.references Martínez, M., García-Nieto, S., Sanchis, J., & Blasco, X. (2009). Genetic algorithms optimization for normalized normal constraint method under Pareto construction. Advances in Engineering Software, 40(4), 260-267. doi:10.1016/j.advengsoft.2008.04.004 es_ES
dc.description.references Mattson, C. A., & Messac, A. (2005). Pareto Frontier Based Concept Selection Under Uncertainty, with Visualization. Optimization and Engineering, 6(1), 85-115. doi:10.1023/b:opte.0000048538.35456.45 es_ES
dc.description.references Mattson, C. A., Mullur, A. A., & Messac, A. (2004). Smart Pareto filter: obtaining a minimal representation of multiobjective design space. Engineering Optimization, 36(6), 721-740. doi:10.1080/0305215042000274942 es_ES
dc.description.references Coello Coello, C. A. (2006). Evolutionary multi-objective optimization: a historical view of the field. IEEE Computational Intelligence Magazine, 1(1), 28-36. doi:10.1109/mci.2006.1597059 es_ES
dc.description.references Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P. N., & Zhang, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 1(1), 32-49. doi:10.1016/j.swevo.2011.03.001 es_ES
dc.description.references Bonissone, P., Subbu, R., & Lizzi, J. (2009). Multicriteria decision making (mcdm): a framework for research and applications. IEEE Computational Intelligence Magazine, 4(3), 48-61. doi:10.1109/mci.2009.933093 es_ES
dc.description.references Laumanns, M., Thiele, L., Deb, K., & Zitzler, E. (2002). Combining Convergence and Diversity in Evolutionary Multiobjective Optimization. Evolutionary Computation, 10(3), 263-282. doi:10.1162/106365602760234108 es_ES
dc.description.references Coello Coello, C. A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 191(11-12), 1245-1287. doi:10.1016/s0045-7825(01)00323-1 es_ES
dc.description.references Mezura-Montes, E., & Coello Coello, C. A. (2011). Constraint-handling in nature-inspired numerical optimization: Past, present and future. Swarm and Evolutionary Computation, 1(4), 173-194. doi:10.1016/j.swevo.2011.10.001 es_ES
dc.description.references ÅSTRÖM, K. J., PANAGOPOULOS, H., & HÄGGLUND, T. (1998). Design of PI Controllers based on Non-Convex Optimization. Automatica, 34(5), 585-601. doi:10.1016/s0005-1098(98)00011-9 es_ES
dc.description.references Reynoso-Meza, G., Sanchis, J., Blasco, X., & Herrero, J. M. (2012). Multiobjective evolutionary algorithms for multivariable PI controller design. Expert Systems with Applications, 39(9), 7895-7907. doi:10.1016/j.eswa.2012.01.111 es_ES


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